We provide additional qualitative results on the real-world TUM dataset [1]} from different viewpoints. We refer readers to the supplementary video for better visualizing the recovered model. As illustrated in Fig. REF  and REF , we can see that the proposed continual neural mapping setting can mitigate the side effect of noisy observations. However, it is also noteworthy that high-frequency signal recovery and denoising are controversial. How to use a single network to recover fine-grained geometry details with sensor noise reduced continually is one interesting extended case for continual neural mapping.
